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Changelog

Source: NEWS.md

Changes in Version 2.1.0 (2023-10-06) :

CRAN release: 2023-10-06

  • new function externVar to perform a secondary regression analysis after the estimation of a primary latent class model
  • new argument pprior in hlme, lcmm, multlcmm and Jointlcmm to fix the probability to belong to each latent class
  • packages survival, parallel, mvtnorm, randtoolbox, marqLevAlg, doParallel, numDeriv are now listed in Imports rather than in Depends
  • no subject-specific predictions in multlcmm with ordinal outcomes
  • corrections in mpjlcmm
  • correction in predictL without random effects
  • correction in epoce and predictY.Jointlcmm
  • use of R’s random number generator in Fortran codes
  • use double precision rather than real(kind=8) in Fortran

Changes in Version 2.0.2 (2023-02-17)

CRAN release: 2023-02-20

  • all vignettes excepted the introduction vignette (now renamed lcmm.Rmd) are removed from the CRAN version because of too long check time.
  • We now provide a website at https://CecileProust-Lima.github.io/lcmm

Changes in Version 2.0.1 (2023-02-01) :

  • new vignette Joint latent class model with Jointlcmm
  • new vignette Multivariate latent class model with mpjlcmm
  • new argument pprior in the hlme function
  • new argument computeDiscrete in the lcmm function
  • mpjlcmm can be used with a mix of hlme/lcmm/multlcmm objects
  • summarytable and summaryplot implement two versions of ICL criterion
  • new output levels in all estimating functions
  • new output varRE in hlme
  • check the convergence of the initial model when using B=random()
  • random parameters are generated with rmnvorm instead of using the Cholesky transformation
  • permut, cuminc, VarCov, coef, vcov functions are available for mpjlcmm objects
  • corrections in mpjlcmm, especially with competing risks
  • correction in residuals for Jointlcmm models
  • bug fixed when using posfix and partialH simultaneously
  • correction in the likelihood for mutlcmm models
  • bug fixed in predictClass and predictRE when using splines
  • verbose=FALSE by default

Changes in Version 2.0.0 (2022-06-15) :

CRAN release: 2022-06-24

  • the model’s estimation is now available in parallel mode!
  • The optimization relies on the parallelized marqLevAlg R package.
  • models with latent classes (ng>1) require initial values
  • the hlme function has now a pprior argument
  • the mpjlcmm function can be used without a time-to-event model
  • the summary functions now shorten the parameters names
  • the log-likelihood functions are now exported
  • bug fixed in mpjlcmm when no random effect is included
  • bug fixed in Jointlcmm with Weibull hazards and competing risks
  • bug fixed in permut when used on Jointlcmm objects with competing risks
  • correction of the outputs of multlcmm models

Changes in Version 1.9.4 (2022-01-03) :

CRAN release: 2022-01-05

  • the multlcmm function is now available for ordinal outcomes (link=“thresholds”) providing a longitudinal IRT model!
  • new vignette Dynamic IRT with multlcmm
  • new dataset simdataHADS
  • new function simulate to simulate a dataset from a hlme, lcmm, multlcmm or Jointlcmm model
  • new functions ItemInfo and plot.ItemInfo to compute and plot Fisher information for ordinal outcomes
  • new argument var.time in the hlme, lcmm, multlcmm and Jointlcmm functions (used in plot(, which=“fit”); issue #91)
  • fix CRAN error with as.vector.data.frame
  • correction in the permut function (transformation parameters were not updated)
  • add envir=parent.frame() in permut and gridsearch to enable the use of these functions in a parallel setting
  • fix bug in the estimation functions with infinite posterior probabilities
  • the gridsearch function now checks that the initial model converged (ie minit$conv=1)
  • the fixef and ranef function are now imported from the nlme package

Changes in Version 1.9.3 (2021-06-17):

CRAN release: 2021-06-21

  • new functions predictClass, predictRE and summaryplot
  • ICL computation in summaryplot
  • use of rmvnorm in multlcmm to generate random initial values
  • maxiter is used in the estimation of the final model in gridsearch
  • fix bug in cuminc without covariates
  • fix bug in the check for numeric type for argument subject with tibbles
  • fix bug in predictY with hlme object when the dataset is named “x”
  • fix bug in the update function when the model has unestimated parameters (posfix)
  • fix bug in hlme when posterior probabilities are NA
  • fix bug in plot with option which=“fit” (observations at the maximum time measurement where not systematically included)
  • correction in the outputs (ppi and resid) of the mpjlcmm function

Changes in Version 1.9.2:

CRAN release: 2020-07-07

  • event variable in joint models can be logical
  • bug fixed in Jointlcmm with prior when there are missing data
  • bug fixed in mpjlcmm : initial values were badly modified (with at least 3 dimensions)
  • small bugs fixed in predictY with median=TRUE

Changes in Version 1.9.1:

CRAN release: 2020-06-03

  • parallel implementation of gridsearch function. Thanks to Raphael Peter for his suggestion.
  • add condRE_Y option in predictYcond
  • add median options in predictY
  • corrections in Jointlcmm, multlcmm and mpjlcmm when prior is specified
  • bugs fixed in some prediction functions
  • small bugs fixed in the summary when some parameters are not estimated
  • bug fixed in VarExpl with models including BM or AR
  • bug fixed in update.mpjlcmm (variance matrix was not correct)
  • manage infinite ppi in hlme
  • correction of epsY type, URL in vignettes, data statements position

Changes in Version 1.8.1:

CRAN release: 2019-06-26

  • new function mpjlcmm for estimating joint latent class models with multiple markers and/or latent processes
  • various post-fit functions for mpjlcmm objects
  • new functions permut and xclass
  • creation of vignettes, thanks to Samy Youbi for his help
  • variable subject must be numeric
  • in plot(which=‘fit’), time intervals do not depend on subset
  • add score test result in summarytable
  • bug fixed in lcmm with prior
  • bug fixed in Jointlcmm with infinite score test
  • bug fixed in dynpred with TimeDepVar

Changes in Version 1.7.9:

CRAN release: 2018-06-22

  • bug in summary when the model did not converge
  • bug in dynpred when draws=TRUE and only 1 horizon or 1 landmark, or when o covariates are included in the survival model, or when using factor
  • bug in Jointlcmm when using B=m1
  • bug in plot.predictY with CI
  • bug in Jointlcmm when B=random(m1)

Changes in Version 1.7.8:

CRAN release: 2017-05-29

  • shades in plot.predictlink/L/Y
  • subset in plot, which=“fit”

Changes in Version 1.7.6 (2016-12-12):

CRAN release: 2016-12-13

  • Small bugs identified and solved in multlcmm

Changes in Version 1.7.5 (2016-03-15):

CRAN release: 2016-03-16

  • Small bugs identified and solved in multlcmm, predictY and predictL

Changes in Version 1.7.4 (2015-12-26):

CRAN release: 2015-12-26

  • The package uses lazydata to automatically load the datasets of the package.

  • jlcmm and mlcmm are shortcuts for functions Jointlcmm and multlcmm, respectively.

  • Function gridsearch provides an automatic grid of departures for reducing the odds of converging towards a local maximum.

  • Initial values can be randomly generated from a model with 1 class (called m1 in next example) with option B=random(m1) in hlme, lcmm, multlcmm and Jointlcmm.

Changes in Version 1.7.3.0 (2015-10-23):

CRAN release: 2015-10-23

  • Functions hlme, lcmm, multlcmm, Jointlcmm now include a posfix option to specify parameters that should not be estimated.

  • Functions lcmm, multlcmm, Jointlcmm now include a partialH option to restrict the computation of the inverse of the Hessian matrix to a submatrix

  • Functions hlme, lcmm, multlcmm, Jointlcmm now allow optional vector B to be an estimated model (with G=1) to reduce calculation time of initial values.

  • Bug identified and solved in calculation of subject-specific predictions in hlme, lcmm, multlcmm and Jointlcmm when cor is not NULL.

  • Bug identified and solved in the calculation of confidence bands for individual dynamic predictions in dynpred with draws=T.

  • Bug identified and solved in the calculation of the explained variance for multlcmm objects when cor is not NULL.

Changes in Version 1.7.1 & 1.7.2 (2015-02-27):

CRAN release: 2015-02-26

  • Function plot now includes a which=“fit” option to plot observed and predicted trajectories stemming from a hlme, lcmm, Jointlcmm or multlcmm object.

  • Function predictlink replaces deprecated function link.confint

  • Function plot gathers deprecated functions plot.linkfunction, plot.baselinerisk, plot.survival, plot.fit together

Changes in Version 1.7.0 (2015-02-13):

  • The function Jointlcmm now allows competing risks data for the survival part and is also available for non-Gaussian longitudinal data. All existing methods for Jointlcmm objects (except EPOCE and Diffepoce functions) are adapted to the new framework.

  • Functions link.confint, plot.linkfunction, predictL are now available for Jointlcmm objects.

  • The new functions incidcum and plot.incidcum respectively compute and plot the cumulative incidence associated to each competing event for Jointlcmm object.

  • The new function fitY computes the marginal predicted values of longitudinal outcomes in their natural scale for lcmm or multlcmm objects.

  • Bug identified and solved in dynpred function when used with a joint model assuming proportional hazards between latent classes.

  • The Makevars file now allows compilation of the package with parallel make.

Changes in Version 1.6.5 & 1.6.6 (2014-09-10):

  • bug solved regarding installation problem with parallel make

Changes in Version 1.6.4 (2014-04-11):

CRAN release: 2014-04-11

  • The new functions dynpred and plot.dynpred respectively compute and plot individual dynamic predictions obtained from a joint latent class model estimated by Jointlcmm.

  • The new function VarCovRE computes the standard errors of the parameters of variance-covariance of the random effects for a hlme, lcmm, Jointlcmm or multlcmm object

  • The new function WaldMult computes multivariate Wald tests and Wald tests for combinations of parameters from hlme, lcmm, Jointlcmm or multlcmm object

  • The new function VarExpl computes the percentages of variance explained by the linear regression for a hlme, lcmm, Jointlclmm or multlcmm object

  • The new functions estimates and VarCov get respectively all parameters estimated and their variance-covariance matrix for a hlme, lcmm, Jointlcmm or multlcmm object

  • Function summary now returns the table containing the results about the fixed effects in the longitudinal model

  • All plots consider now the … options

  • Functions plot.linkfunction and plot.predict have now an add argument

  • Function multlcmm now allows “splines” or “Splines” specification for the link functions

  • Functions lcmm and multlcmm now compute the transformations even if the maximum number of iterations is reached without convergence

  • bug identified and solved in multlcmm when the response variables are not integers

  • bug identified and solved in multlcmm when using contrast

  • bug identified and solved in plot.linkfunction for the y axes positions

  • bug identified and solved in hlme, lcmm, Jointlcmm and multlcmm when including interactions in mixture.

Changes in Version 1.6.2 (2013-03-06):

CRAN release: 2013-03-07

  • The new function multlcmm now estimates latent process mixed models for multivariate curvilinear longitudinal outcomes (with link functions: linear, beta or splines). Various post-fit computation and output functions are also available including plot.linkfunction, predictY, predictL, etc

  • All the functions hlme, lcmm, Jointlcmm include a cor option for including a brownian motion or a first-order autoregressive error process in addition to the independent errors of measurement

  • bug identified and solved in predictL, predictY and plot.predict when used with factor covariate

Changes in Version 1.5.8 (2012-10-01):

CRAN release: 2012-10-04

  • bug identified and solved in predictY.lcmm when used with a splines link function and an outcome with minimum value not at 0

Changes in Version 1.5.7 (2012-07-24):

CRAN release: 2012-07-24

  • The function predictY now computes the predicted values (possibly class-specific) of the longitudinal outcome not only from a lcmm object but also from a hlme or a Jointlcmm object for a specified profile of covariates.

  • bug identified and solved in predictY.lcmm when used with a threshold link function and a Monte Carlo method

Changes in Version 1.5.6 (2012-07-16):

CRAN release: 2012-07-16

  • missing data handled in hlme, lcmm and Jointlcmm using na.action with attributes 1 for na.omit or 2 for na.fail

  • The new function predictY.lcmm computes predicted values of a lcmm object in the natural outcome scale for a specified profile of covariates, and also provides confidence bands using a Monte Carlo method.

  • bugs in epoce computation solved (with splines baseline risk function, and/or NaN values under solaris system)

  • bug identified and solved in summary functions regarding the labels of covariate effects in peculiar cases

Changes in Version 1.5.2 (2012-04-06):

CRAN release: 2012-04-16

  • improved variable specification in the estimating functions Jointlcmm, lcmm and hlme with
    • categorical variables using factor()
    • variables entered as functions using I()
    • interaction terms using "*" and “:”

  • computation of the predictive accuracy measure EPOCE from a Jointlcmm object either on the training data or on external data (post-fit functions epoce and Diffepoce)

  • for discrete outcomes, lcmm function now computates the posterior discrete log-likelihood and the universal approximate cross-validation criterion (UACV)

  • Jointlcmm now includes two parameterizations of I-splines and piecewise-constant baseline risks functions to ensure positive risks: either log/exp or sqrt/square (option logscale=).